mmclassification/configs/efficientnet/efficientnet-es_8xb32-01nor...

30 lines
923 B
Python

_base_ = [
'../_base_/models/efficientnet_es.py',
'../_base_/datasets/imagenet_bs32.py',
'../_base_/schedules/imagenet_bs256.py',
'../_base_/default_runtime.py',
]
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetRandomCrop', scale=224),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackClsInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='EfficientNetCenterCrop', crop_size=224),
dict(type='PackClsInputs'),
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (32 samples per GPU)
auto_scale_lr = dict(base_batch_size=256)